Once the basic single-particle loop was stable, it was time to scale up the complexity to see how the system handled a more realistic, crowded environment. I upgraded the core engine to process a chaotic intake cloud of 50 distinct microplastic particles, all spawning at completely randomized vertical entry points to simulate a true messy current. Throwing that many objects at a high flow velocity of 250 millimeters per second immediately triggered a classic simulation glitch called tunneling, where particles move so fast between frames that they literally skip straight through solid walls without registering a collision.

To fix this boundary breakdown, I implemented a sub-stepping physics processor that cracks open each standard time frame and divides it into 10 microscopic intervals, dropping the delta time down to a razor-sharp one-thousandth of a second. Alongside the processing fix, I overhauled the raker profiles from flat shapes into true downstream-slanted fins using a two-slope geometric matrix that features a gentle leading edge paired with a steep trailing edge. This combination of tighter time tracking and accurate geometry paid off completely, with the high-fidelity engine locking in a mathematically verified 100% filtration efficiency yield and proving that the bio-inspired boundary layer forces can successfully override downward suction to execute clean ricochet separation.

Honestly, working through these complex physics anomalies and structuring the data loops has made things click in a way they never had before, and I am really starting to feel like I’m getting the hang of programming now. My confidence with Python is hitting a whole new level, especially because I have been leveraging AI as an interactive coding partner throughout this entire sprint. Instead of just staring at errors or trying to guess why a boundary layer isn’t registering, I use the AI to brainstorm script architectures, debug tricky syntax bottlenecks, and deeply understand how to properly execute those micro-stepping intervals. It honestly feels like having a senior developer sitting right next to me, helping me bridge the gap between the fluid dynamics math in my head and the actual code, which has completely accelerated how fast I can learn and ship clean software.